Paper Contents
Abstract
ABSTRACT The rapid proliferation of deepfake technologies poses significant threats to digital security, including identity theft, impersonation fraud, and social engineering attacks. This project presents Enhanced CloneCatcher, an AI-powered system designed to detect deepfake media in real-time, offering advanced security mechanisms for critical sectors such as finance, law enforcement, and social media. The system combines deep learning techniques (CNNs, RNNs, and attention mechanisms) and real-time voice authentication with anomaly detection frameworks. Enhanced features include multi-factor AI authentication, voice biometrics, and facial micro-expression analysis. It also incorporates real-time alerting and visual output to aid cybersecurity professionals in forensic analysis. Our evaluation demonstrates a detection accuracy of over 92% on benchmark datasets like DFDC and ASVspoof, positioning CloneCatcher as a powerful tool in combating deepfake-based cybercrime.
Copyright
Copyright © 2025 VISHNU RAM M. This is an open access article distributed under the Creative Commons Attribution License.
